Dynamic system identification and sensor linearization using neural network techniques

Abstract

Many techniques have been proposed for the identification of unknown system. The scope of the parameter approximation or estimation and system identification is growing day by day. Lots of research has been done in this field but it can be still considered as an open field for researchers. The overall field of system identification is day by day growing in the field of research and lots of methods are coming time to time. This research presents a number of results, examples and applications of parameter identification techniques. Different Methods are introduced here with less and more complexities. For System Identification some of Neural Network techniques are studied. Least mean square technique is used for the final calculations of simulation results. Simulations are done with the help of Matlab programming. Some Neural Network Techniques have been proposed here are multilayered neural Network, Functional link Layer Neural network Technique. Mainly Disadvantage of basic system identification techniques is that it use the back propagation techniques for the weight updating purpose which have a lots of computation complexity. A single layer Artificial Neural Network has been studied which is known as Functional Link Artificial Neural Network (FLANN). In such type of System Identification technique hidden layers are wipe out by functional expansion of input pattern. The prominent advantage of such type of network is that the computation complexity is much less than complexity of the multilayered neural network. In the field Control and Instrumentation there are some characteristics which are desirable for the sensors.